Application of improved C-V segmentation in multi-spectral imager

In order to improve the image segmentation speed when using C-V model, reduce the segmentation coupling with initial contour position, and improve the image segmentation efficiency of multi-spectral imager, an improved C-V model is proposed in the paper. In this model, the Dirac function' parameter is corrected adaptively by introducing the maximum value of distance function in each iteration. In this way, the effective range of active contour is broadened, and the number of iterations is reduced. The experimental results show that the ideal segmentation effect is obtained by the improved C-V model with the iteration termination condition. Compared with the classic C-V model, the influence of initial contour position on segmentation is reduced. In addition, the convergence speed is improved by 7 times. The characteristics of real time and global nature both become better. Therefore, the robustness of multi-spectral imager segmentation is improved accordingly.